CSE 521S Reading List
Clinical Monitoring
-
O. Chipara, C. Lu, T.C. Bailey and G.-C. Roman,
Reliable Clinical Monitoring using Wireless Sensor Networks: Experience in a Step-down Hospital Unit,
ACM Conference on Embedded Networked Sensor Systems (SenSys'10), November 2010.
Critique #1
-
R. Dor, G. Hackmann, Z. Yang, C. Lu, Y. Chen, M. Kollef and T.C. Bailey,
Experiences with an End-To-End Wireless Clinical Monitoring System,
Conference on Wireless Health (WH'12), October 2012.
-
J. Zhang, R. Dai, A. Rjob, R. Wang, R. Hamauon, J. Candell, T. Bailey, V.J. Fraser, M.C. Vazquez Guillamet, and C. Lu,
Contact Tracing for Healthcare Workers in an Intensive Care Unit,
Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (UbiComp'23), 7(3), Article 141, September 2023.
-
[News]
Wireless Network in Hospital Monitors Vital Signs
-
[News]
Wireless Clinical Monitoring Paper Received SenSys Test of Time Award
Predict Clinical Outcomes with Wearables
-
R. Dai, T Kannampallil, S. Kim, V. Thornton, L. Bierut, C. Lu,
Detecting Mental Disorders with Wearables: A Large Cohort Study,
ACM/IEEE Conference on Internet of Things Design and Implementation (IoTDI'23), May 2023.
Critique #2
-
J. Zhang, D. Li, R. Dai, H. Cos, G.A. Williams, L. Raper, C.W. Hammill, and C. Lu,
Predicting Post-Operative Complications with Wearables: A Case Study with Patients Undergoing Pancreatic Surgery,
Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (UbiComp'22), 6(2), Article 87, July 2022.
Critique #3
-
R. Dai, T. Kannampallil, J. Zhang, N. Lv, J. Ma, and C. Lu,
Multi-Task Learning for Randomized Controlled Trials: A Case Study on Predicting Depression with Wearable Data,
Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (UbiComp'22), 6(2), Article 50, July 2022.
-
R. Dai, C. Lu, M. Avidan, and T. Kannampallil,
RespWatch: Robust Measurement of Respiratory Rate on Smartwatches with Photoplethysmography,
ACM/IEEE International Conference on Internet of Things Design and Implementation (IoTDI'21), May 2021.
-
[News]
Data from Wearables Could Be a Boon to Mental Health Diagnosis
-
[News]
Wearable Fitness Trackers Help Physicians Track Patient Health
-
[News]
Personalized Prediction of Depression Treatment Outcomes with Wearables
Predict Clinician Burnout
-
S.S. Lou, D. Lew, D.R. Harford, C. Lu, B.A. Evanoff, J.G. Duncan, and T. Kannampallil,
Temporal Associations Between EHR-Derived Workload, Burnout, and Errors: a Prospective Cohort Study,
Journal of General Internal Medicine, June 2022.
-
S.S. Lou, H. Liu, B.C. Warner, D. Harford, C. Lu, and T. Kannampallil,
Predicting Physician Burnout using Clinical Activity Logs: Model Performance and Lessons Learned,
Journal of Biomedical Informatics, Volume 127, 2022.
-
H. Liu, S.S. Lou, B. Warner, D.R. Harford, T. Kannampallil, and C. Lu,
HiPAL: A Deep Framework for Physician Burnout Prediction Using Activity Logs in Electronic Health Records,
ACM SIGKDD Conference on Knowledge Discovery & Data Mining (KDD'22), August 2022.
-
[News]
Learning Physician Burnout from Electronic Health Record Activities
AI for Critical Care
-
B. Xue, N. Shah, H. Yang, T. Kannampallil, P.R.O. Payne, C. Lu, A.S. Said,
Multi-horizon Predictive Models for Guiding Extracorporeal Resource Allocation in Critically Ill COVID-19 Patients,
Journal of the American Medical Informatics Association, 30(4):656-667, April 2023.
Critique #4
-
B. Xue, A. Said, Z. Xu, H. Liu, N. Shah, H. Yang, P. Payne, C. Lu,
Assisting Clinical Decisions for Scarcely Available Treatment via Disentangled Latent Representation,
ACM SIGKDD Conference on Knowledge Discovery & Data Mining (KDD'23), August 2023.
-
[News]
Artificial Intelligence May Assist Decisions on Which Patients Should Get Critical Life Support
AI for Perioperative Care
-
S.S. Lou, H. Liu, C. Lu, T.S. Wildes, B.L. Hall, and T. Kannampallil,
Personalized Surgical Transfusion Risk Prediction Using Machine Learning to Guide
Preoperative Type and Screen Orders,
Anesthesiology, Vol. 137, 55-66, July 2022.
-
B. Xue, D. Li, C. Lu, C.R. King, T. Wildes, M.S. Avidan, T. Kannampallil, and J. Abraham,
Use of Machine Learning to Develop and Evaluate Models Using Preoperative and
Intraoperative Data to Identify Risks of Postoperative Complications,
JAMA Network Open, 4(3):e212240, 2021.
Critique #5
-
J. Abraham, B. Bartek, A. Meng, C. Ryan King, B. Xue, C. Lu, M. Avidan,
Integrating Machine Learning Predictions for Perioperative Risk Management: Towards an
Empirical Design of a Flexible-Standardized Risk Assessment Tool,
Journal of Biomedical Informatics, Volume 137, 2023.
-
B. Xue, Y. Jiao, T. Kannampallil, B. Fritz, C. King, J. Abraham, M. Avidan, and C. Lu,
Perioperative Predictions with Interpretable Latent Representation,
ACM SIGKDD Conference on Knowledge Discovery & Data Mining (KDD'22), August 2022.
-
[News]
Predicting Surgical Outcomes with Machine Learning